The work introduces Org-Agent, a constraint-centric reasoning framework that decomposes a task into atomic subtasks and builds a Task Dependency Graph (TDG), schedules subtasks by topological order, and executes each subtask under constraint rules with evidence-acquisition and memory-management tools; it reaches 44.03% average accuracy on GroupMemBench versus 37.72% for BM25 and 38.79% for the strongest memory system Hindsight, and improves MUSES-Bench averages by 8.27, 4.24, and 5.29 points across three backbones, with ablations supporting both dependency modeling and tool use.
The work introduces Org-Agent, a constraint-centric reasoning framework that decomposes a task into atomic subtasks and builds a Task Dependency Graph (TDG), schedules subtasks by topological order, and executes each subtask under constraint rules with evidence-acquisition and memory-management tools; it reaches 44.03% average accuracy on GroupMemBench versus 37.72% for BM25 and 38.79% for the strongest memory system Hindsight, and improves MUSES-Bench averages by 8.27, 4.24, and 5.29 points across three backbones, with ablations supporting both dependency modeling and tool use.